Displaying publications 1 - 20 of 116 in total

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  1. Identification of metabolic pathways using pathfinding approaches: a systematic review
    Abd Algfoor Z, Shahrizal Sunar M, Abdullah A, Kolivand H
    Brief Funct Genomics, 2017 03 01;16(2):87-98.
    PMID: 26969656 DOI: 10.1093/bfgp/elw002
    Metabolic pathways have become increasingly available for various microorganisms. Such pathways have spurred the development of a wide array of computational tools, in particular, mathematical pathfinding approaches. This article can facilitate the understanding of computational analysis of metabolic pathways in genomics. Moreover, stoichiometric and pathfinding approaches in metabolic pathway analysis are discussed. Three major types of studies are elaborated: stoichiometric identification models, pathway-based graph analysis and pathfinding approaches in cellular metabolism. Furthermore, evaluation of the outcomes of the pathways with mathematical benchmarking metrics is provided. This review would lead to better comprehension of metabolism behaviors in living cells, in terms of computed pathfinding approaches.
    Matched MeSH terms: Computational Biology/methods*
  2. Fulltext An improved swarm optimization for parameter estimation and biological model selection
    Abdullah A, Deris S, Mohamad MS, Anwar S
    PLoS One, 2013;8(4):e61258.
    PMID: 23593445 DOI: 10.1371/journal.pone.0061258
    One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.
    Matched MeSH terms: Computational Biology/methods*
  3. Fulltext Comparative genome-wide analysis of WRKY, MADS-box and MYB transcription factor families in Arabidopsis and rice
    Abdullah-Zawawi MR, Ahmad-Nizammuddin NF, Govender N, Harun S, Mohd-Assaad N, Mohamed-Hussein ZA
    Sci Rep, 2021 10 04;11(1):19678.
    PMID: 34608238 DOI: 10.1038/s41598-021-99206-y
    Transcription factors (TFs) form the major class of regulatory genes and play key roles in multiple plant stress responses. In most eukaryotic plants, transcription factor (TF) families (WRKY, MADS-box and MYB) activate unique cellular-level abiotic and biotic stress-responsive strategies, which are considered as key determinants for defense and developmental processes. Arabidopsis and rice are two important representative model systems for dicot and monocot plants, respectively. A comprehensive comparative study on 101 OsWRKY, 34 OsMADS box and 122 OsMYB genes (rice genome) and, 71 AtWRKY, 66 AtMADS box and 144 AtMYB genes (Arabidopsis genome) showed various relationships among TFs across species. The phylogenetic analysis clustered WRKY, MADS-box and MYB TF family members into 10, 7 and 14 clades, respectively. All clades in WRKY and MYB TF families and almost half of the total number of clades in the MADS-box TF family are shared between both species. Chromosomal and gene structure analysis showed that the Arabidopsis-rice orthologous TF gene pairs were unevenly localized within their chromosomes whilst the distribution of exon-intron gene structure and motif conservation indicated plausible functional similarity in both species. The abiotic and biotic stress-responsive cis-regulatory element type and distribution patterns in the promoter regions of Arabidopsis and rice WRKY, MADS-box and MYB orthologous gene pairs provide better knowledge on their role as conserved regulators in both species. Co-expression network analysis showed the correlation between WRKY, MADs-box and MYB genes in each independent rice and Arabidopsis network indicating their role in stress responsiveness and developmental processes.
    Matched MeSH terms: Computational Biology/methods
  4. Shotgun Proteomics and Mass Spectrometry as a Tool for Protein Identification and Profiling of Bio-Carrier-Based Therapeutics on Human Cancer Cells
    Abidin SAZ, Othman I, Naidu R
    Methods Mol Biol, 2021;2211:233-240.
    PMID: 33336281 DOI: 10.1007/978-1-0716-0943-9_16
    Shotgun proteomics has been widely applied to study proteins in complex biological samples. Combination of high-performance liquid chromatography with mass spectrometry has allowed for comprehensive protein analysis with high resolution, sensitivity, and mass accuracy. Prior to mass spectrometry analysis, proteins are extracted from biological samples and subjected to in-solution trypsin digestion. The digested proteins are subjected for clean-up and injected into the liquid chromatography-mass spectrometry system for peptide mass identification. Protein identification is performed by analyzing the mass spectrometry data on a protein search engine software such as PEAKS studio loaded with protein database for the species of interest. Results such as protein score, protein coverage, number of peptides, and unique peptides identified will be obtained and can be used to determine proteins identified with high confidence. This method can be applied to understand the proteomic changes or profile brought by bio-carrier-based therapeutics in vitro. In this chapter, we describe methods in which proteins can be extracted for proteomic analysis using a shotgun approach. The chapter outlines important in vitro techniques and data analysis that can be applied to investigate the proteome dynamics.
    Matched MeSH terms: Computational Biology/methods
  5. A biological inspired fuzzy adaptive window median filter (FAWMF) for enhancing DNA signal processing
    Ahmad M, Jung LT, Bhuiyan AA
    Comput Methods Programs Biomed, 2017 Oct;149:11-17.
    PMID: 28802326 DOI: 10.1016/j.cmpb.2017.06.021
    BACKGROUND AND OBJECTIVE: Digital signal processing techniques commonly employ fixed length window filters to process the signal contents. DNA signals differ in characteristics from common digital signals since they carry nucleotides as contents. The nucleotides own genetic code context and fuzzy behaviors due to their special structure and order in DNA strand. Employing conventional fixed length window filters for DNA signal processing produce spectral leakage and hence results in signal noise. A biological context aware adaptive window filter is required to process the DNA signals.

    METHODS: This paper introduces a biological inspired fuzzy adaptive window median filter (FAWMF) which computes the fuzzy membership strength of nucleotides in each slide of window and filters nucleotides based on median filtering with a combination of s-shaped and z-shaped filters. Since coding regions cause 3-base periodicity by an unbalanced nucleotides' distribution producing a relatively high bias for nucleotides' usage, such fundamental characteristic of nucleotides has been exploited in FAWMF to suppress the signal noise.

    RESULTS: Along with adaptive response of FAWMF, a strong correlation between median nucleotides and the Π shaped filter was observed which produced enhanced discrimination between coding and non-coding regions contrary to fixed length conventional window filters. The proposed FAWMF attains a significant enhancement in coding regions identification i.e. 40% to 125% as compared to other conventional window filters tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms.

    CONCLUSION: This study proves that conventional fixed length window filters applied to DNA signals do not achieve significant results since the nucleotides carry genetic code context. The proposed FAWMF algorithm is adaptive and outperforms significantly to process DNA signal contents. The algorithm applied to variety of DNA datasets produced noteworthy discrimination between coding and non-coding regions contrary to fixed window length conventional filters.

    Matched MeSH terms: Computational Biology/methods*
  6. Thermodynamic heuristics with case-based reasoning: combined insights for RNA pseudoknot secondary structure
    Al-Khatib RM, Rashid NA, Abdullah R
    J Biomol Struct Dyn, 2011 Aug;29(1):1-26.
    PMID: 21696223
    The secondary structure of RNA pseudoknots has been extensively inferred and scrutinized by computational approaches. Experimental methods for determining RNA structure are time consuming and tedious; therefore, predictive computational approaches are required. Predicting the most accurate and energy-stable pseudoknot RNA secondary structure has been proven to be an NP-hard problem. In this paper, a new RNA folding approach, termed MSeeker, is presented; it includes KnotSeeker (a heuristic method) and Mfold (a thermodynamic algorithm). The global optimization of this thermodynamic heuristic approach was further enhanced by using a case-based reasoning technique as a local optimization method. MSeeker is a proposed algorithm for predicting RNA pseudoknot structure from individual sequences, especially long ones. This research demonstrates that MSeeker improves the sensitivity and specificity of existing RNA pseudoknot structure predictions. The performance and structural results from this proposed method were evaluated against seven other state-of-the-art pseudoknot prediction methods. The MSeeker method had better sensitivity than the DotKnot, FlexStem, HotKnots, pknotsRG, ILM, NUPACK and pknotsRE methods, with 79% of the predicted pseudoknot base-pairs being correct.
    Matched MeSH terms: Computational Biology/methods*
  7. Fulltext TranCEP: Predicting the substrate class of transmembrane transport proteins using compositional, evolutionary, and positional information
    Alballa M, Aplop F, Butler G
    PLoS One, 2020;15(1):e0227683.
    PMID: 31935244 DOI: 10.1371/journal.pone.0227683
    Transporters mediate the movement of compounds across the membranes that separate the cell from its environment and across the inner membranes surrounding cellular compartments. It is estimated that one third of a proteome consists of membrane proteins, and many of these are transport proteins. Given the increase in the number of genomes being sequenced, there is a need for computational tools that predict the substrates that are transported by the transmembrane transport proteins. In this paper, we present TranCEP, a predictor of the type of substrate transported by a transmembrane transport protein. TranCEP combines the traditional use of the amino acid composition of the protein, with evolutionary information captured in a multiple sequence alignment (MSA), and restriction to important positions of the alignment that play a role in determining the specificity of the protein. Our experimental results show that TranCEP significantly outperforms the state-of-the-art predictors. The results quantify the contribution made by each type of information used.
    Matched MeSH terms: Computational Biology/methods*
  8. Fulltext Identification of NRAS Diagnostic Biomarkers and Drug Targets for Endometrial Cancer-An Integrated in Silico Approach
    Alessandro L, Low KE, Abushelaibi A, Lim SE, Cheng WH, Chang SK, et al.
    Int J Mol Sci, 2022 Nov 18;23(22).
    PMID: 36430761 DOI: 10.3390/ijms232214285
    The diagnosis of endometrial cancer involves sequential, invasive tests to assess the thickness of the endometrium by a transvaginal ultrasound scan. In 6−33% of cases, endometrial biopsy results in inadequate tissue for a conclusive pathological diagnosis and 6% of postmenopausal women with non-diagnostic specimens are later discovered to have severe endometrial lesions. Thus, identifying diagnostic biomarkers could offer a non-invasive diagnosis for community or home-based triage of symptomatic or asymptomatic women. Herein, this study identified high-risk pathogenic nsSNPs in the NRAS gene. The nsSNPs of NRAS were retrieved from the NCBI database. PROVEAN, SIFT, PolyPhen-2, SNPs&GO, PhD-SNP and PANTHER were used to predict the pathogenicity of the nsSNPs. Eleven nsSNPs were identified as “damaging”, and further stability analysis using I-Mutant 2.0 and MutPred 2 indicated eight nsSNPs to cause decreased stability (DDG scores < −0.5). Post-translational modification and protein−protein interactions (PPI) analysis showed putative phosphorylation sites. The PPI network indicated a GFR-MAPK signalling pathway with higher node degrees that were further evaluated for drug targets. The P34L, G12C and Y64D showed significantly lower binding affinity towards GTP than wild-type. Furthermore, the Kaplan−Meier bioinformatics analyses indicated that the NRAS gene deregulation affected the overall survival rate of patients with endometrial cancer, leading to prognostic significance. Findings from this could be considered novel diagnostic and therapeutic markers.
    Matched MeSH terms: Computational Biology/methods
  9. Fulltext The challenges of designing a benchmark strategy for bioinformatics pipelines in the identification of antimicrobial resistance determinants using next generation sequencing technologies
    Angers-Loustau A, Petrillo M, Bengtsson-Palme J, Berendonk T, Blais B, Chan KG, et al.
    F1000Res, 2018;7.
    PMID: 30026930 DOI: 10.12688/f1000research.14509.2
    Next-Generation Sequencing (NGS) technologies are expected to play a crucial role in the surveillance of infectious diseases, with their unprecedented capabilities for the characterisation of genetic information underlying the virulence and antimicrobial resistance (AMR) properties of microorganisms.  In the implementation of any novel technology for regulatory purposes, important considerations such as harmonisation, validation and quality assurance need to be addressed.  NGS technologies pose unique challenges in these regards, in part due to their reliance on bioinformatics for the processing and proper interpretation of the data produced.  Well-designed benchmark resources are thus needed to evaluate, validate and ensure continued quality control over the bioinformatics component of the process.  This concept was explored as part of a workshop on "Next-generation sequencing technologies and antimicrobial resistance" held October 4-5 2017.   Challenges involved in the development of such a benchmark resource, with a specific focus on identifying the molecular determinants of AMR, were identified. For each of the challenges, sets of unsolved questions that will need to be tackled for them to be properly addressed were compiled. These take into consideration the requirement for monitoring of AMR bacteria in humans, animals, food and the environment, which is aligned with the principles of a "One Health" approach.
    Matched MeSH terms: Computational Biology/methods*
  10. Allele Mining Strategies: Principles and Utilisation for Blast Resistance Genes in Rice (Oryza sativa L.)
    Ashkani S, Yusop MR, Shabanimofrad M, Azady A, Ghasemzadeh A, Azizi P, et al.
    Curr Issues Mol Biol, 2015;17:57-73.
    PMID: 25706446
    Allele mining is a promising way to dissect naturally occurring allelic variants of candidate genes with essential agronomic qualities. With the identification, isolation and characterisation of blast resistance genes in rice, it is now possible to dissect the actual allelic variants of these genes within an array of rice cultivars via allele mining. Multiple alleles from the complex locus serve as a reservoir of variation to generate functional genes. The routine sequence exchange is one of the main mechanisms of R gene evolution and development. Allele mining for resistance genes can be an important method to identify additional resistance alleles and new haplotypes along with the development of allele-specific markers for use in marker-assisted selection. Allele mining can be visualised as a vital link between effective utilisation of genetic and genomic resources in genomics-driven modern plant breeding. This review studies the actual concepts and potential of mining approaches for the discovery of alleles and their utilisation for blast resistance genes in rice. The details provided here will be important to provide the rice breeder with a worthwhile introduction to allele mining and its methodology for breakthrough discovery of fresh alleles hidden in hereditary diversity, which is vital for crop improvement.
    Matched MeSH terms: Computational Biology/methods
  11. Fulltext An efficient and robust laboratory workflow and tetrapod database for larger scale environmental DNA studies
    Axtner J, Crampton-Platt A, Hörig LA, Mohamed A, Xu CCY, Yu DW, et al.
    Gigascience, 2019 Apr 01;8(4).
    PMID: 30997489 DOI: 10.1093/gigascience/giz029
    BACKGROUND: The use of environmental DNA for species detection via metabarcoding is growing rapidly. We present a co-designed lab workflow and bioinformatic pipeline to mitigate the 2 most important risks of environmental DNA use: sample contamination and taxonomic misassignment. These risks arise from the need for polymerase chain reaction (PCR) amplification to detect the trace amounts of DNA combined with the necessity of using short target regions due to DNA degradation.

    FINDINGS: Our high-throughput workflow minimizes these risks via a 4-step strategy: (i) technical replication with 2 PCR replicates and 2 extraction replicates; (ii) using multi-markers (12S,16S,CytB); (iii) a "twin-tagging," 2-step PCR protocol; and (iv) use of the probabilistic taxonomic assignment method PROTAX, which can account for incomplete reference databases. Because annotation errors in the reference sequences can result in taxonomic misassignment, we supply a protocol for curating sequence datasets. For some taxonomic groups and some markers, curation resulted in >50% of sequences being deleted from public reference databases, owing to (i) limited overlap between our target amplicon and reference sequences, (ii) mislabelling of reference sequences, and (iii) redundancy. Finally, we provide a bioinformatic pipeline to process amplicons and conduct PROTAX assignment and tested it on an invertebrate-derived DNA dataset from 1,532 leeches from Sabah, Malaysia. Twin-tagging allowed us to detect and exclude sequences with non-matching tags. The smallest DNA fragment (16S) amplified most frequently for all samples but was less powerful for discriminating at species rank. Using a stringent and lax acceptance criterion we found 162 (stringent) and 190 (lax) vertebrate detections of 95 (stringent) and 109 (lax) leech samples.

    CONCLUSIONS: Our metabarcoding workflow should help research groups increase the robustness of their results and therefore facilitate wider use of environmental and invertebrate-derived DNA, which is turning into a valuable source of ecological and conservation information on tetrapods.

    Matched MeSH terms: Computational Biology/methods
  12. A review on the computational approaches for gene regulatory network construction
    Chai LE, Loh SK, Low ST, Mohamad MS, Deris S, Zakaria Z
    Comput Biol Med, 2014 May;48:55-65.
    PMID: 24637147 DOI: 10.1016/j.compbiomed.2014.02.011
    Many biological research areas such as drug design require gene regulatory networks to provide clear insight and understanding of the cellular process in living cells. This is because interactions among the genes and their products play an important role in many molecular processes. A gene regulatory network can act as a blueprint for the researchers to observe the relationships among genes. Due to its importance, several computational approaches have been proposed to infer gene regulatory networks from gene expression data. In this review, six inference approaches are discussed: Boolean network, probabilistic Boolean network, ordinary differential equation, neural network, Bayesian network, and dynamic Bayesian network. These approaches are discussed in terms of introduction, methodology and recent applications of these approaches in gene regulatory network construction. These approaches are also compared in the discussion section. Furthermore, the strengths and weaknesses of these computational approaches are described.
    Matched MeSH terms: Computational Biology/methods*
  13. Fulltext Evidence-based gene models for structural and functional annotations of the oil palm genome
    Chan KL, Tatarinova TV, Rosli R, Amiruddin N, Azizi N, Halim MAA, et al.
    Biol. Direct, 2017 Sep 08;12(1):21.
    PMID: 28886750 DOI: 10.1186/s13062-017-0191-4
    BACKGROUND: Oil palm is an important source of edible oil. The importance of the crop, as well as its long breeding cycle (10-12 years) has led to the sequencing of its genome in 2013 to pave the way for genomics-guided breeding. Nevertheless, the first set of gene predictions, although useful, had many fragmented genes. Classification and characterization of genes associated with traits of interest, such as those for fatty acid biosynthesis and disease resistance, were also limited. Lipid-, especially fatty acid (FA)-related genes are of particular interest for the oil palm as they specify oil yields and quality. This paper presents the characterization of the oil palm genome using different gene prediction methods and comparative genomics analysis, identification of FA biosynthesis and disease resistance genes, and the development of an annotation database and bioinformatics tools.

    RESULTS: Using two independent gene-prediction pipelines, Fgenesh++ and Seqping, 26,059 oil palm genes with transcriptome and RefSeq support were identified from the oil palm genome. These coding regions of the genome have a characteristic broad distribution of GC3 (fraction of cytosine and guanine in the third position of a codon) with over half the GC3-rich genes (GC3 ≥ 0.75286) being intronless. In comparison, only one-seventh of the oil palm genes identified are intronless. Using comparative genomics analysis, characterization of conserved domains and active sites, and expression analysis, 42 key genes involved in FA biosynthesis in oil palm were identified. For three of them, namely EgFABF, EgFABH and EgFAD3, segmental duplication events were detected. Our analysis also identified 210 candidate resistance genes in six classes, grouped by their protein domain structures.

    CONCLUSIONS: We present an accurate and comprehensive annotation of the oil palm genome, focusing on analysis of important categories of genes (GC3-rich and intronless), as well as those associated with important functions, such as FA biosynthesis and disease resistance. The study demonstrated the advantages of having an integrated approach to gene prediction and developed a computational framework for combining multiple genome annotations. These results, available in the oil palm annotation database ( http://palmxplore.mpob.gov.my ), will provide important resources for studies on the genomes of oil palm and related crops.

    REVIEWERS: This article was reviewed by Alexander Kel, Igor Rogozin, and Vladimir A. Kuznetsov.

    Matched MeSH terms: Computational Biology/methods
  14. Identification of informative genes and pathways using an improved penalized support vector machine with a weighting scheme
    Chan WH, Mohamad MS, Deris S, Zaki N, Kasim S, Omatu S, et al.
    Comput Biol Med, 2016 10 01;77:102-15.
    PMID: 27522238 DOI: 10.1016/j.compbiomed.2016.08.004
    Incorporation of pathway knowledge into microarray analysis has brought better biological interpretation of the analysis outcome. However, most pathway data are manually curated without specific biological context. Non-informative genes could be included when the pathway data is used for analysis of context specific data like cancer microarray data. Therefore, efficient identification of informative genes is inevitable. Embedded methods like penalized classifiers have been used for microarray analysis due to their embedded gene selection. This paper proposes an improved penalized support vector machine with absolute t-test weighting scheme to identify informative genes and pathways. Experiments are done on four microarray data sets. The results are compared with previous methods using 10-fold cross validation in terms of accuracy, sensitivity, specificity and F-score. Our method shows consistent improvement over the previous methods and biological validation has been done to elucidate the relation of the selected genes and pathway with the phenotype under study.
    Matched MeSH terms: Computational Biology/methods*
  15. Fulltext A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides
    Charoenkwan P, Chotpatiwetchkul W, Lee VS, Nantasenamat C, Shoombuatong W
    Sci Rep, 2021 Dec 10;11(1):23782.
    PMID: 34893688 DOI: 10.1038/s41598-021-03293-w
    Owing to their ability to maintain a thermodynamically stable fold at extremely high temperatures, thermophilic proteins (TTPs) play a critical role in basic research and a variety of applications in the food industry. As a result, the development of computation models for rapidly and accurately identifying novel TTPs from a large number of uncharacterized protein sequences is desirable. In spite of existing computational models that have already been developed for characterizing thermophilic proteins, their performance and interpretability remain unsatisfactory. We present a novel sequence-based thermophilic protein predictor, termed SCMTPP, for improving model predictability and interpretability. First, an up-to-date and high-quality dataset consisting of 1853 TPPs and 3233 non-TPPs was compiled from published literature. Second, the SCMTPP predictor was created by combining the scoring card method (SCM) with estimated propensity scores of g-gap dipeptides. Benchmarking experiments revealed that SCMTPP had a cross-validation accuracy of 0.883, which was comparable to that of a support vector machine-based predictor (0.906-0.910) and 2-17% higher than that of commonly used machine learning models. Furthermore, SCMTPP outperformed the state-of-the-art approach (ThermoPred) on the independent test dataset, with accuracy and MCC of 0.865 and 0.731, respectively. Finally, the SCMTPP-derived propensity scores were used to elucidate the critical physicochemical properties for protein thermostability enhancement. In terms of interpretability and generalizability, comparative results showed that SCMTPP was effective for identifying and characterizing TPPs. We had implemented the proposed predictor as a user-friendly online web server at http://pmlabstack.pythonanywhere.com/SCMTPP in order to allow easy access to the model. SCMTPP is expected to be a powerful tool for facilitating community-wide efforts to identify TPPs on a large scale and guiding experimental characterization of TPPs.
    Matched MeSH terms: Computational Biology/methods*
  16. Fulltext Identification of four functionally important microRNA families with contrasting differential expression profiles between drought-tolerant and susceptible rice leaf at vegetative stage
    Cheah BH, Nadarajah K, Divate MD, Wickneswari R
    BMC Genomics, 2015;16:692.
    PMID: 26369665 DOI: 10.1186/s12864-015-1851-3
    Developing drought-tolerant rice varieties with higher yield under water stressed conditions provides a viable solution to serious yield-reduction impact of drought. Understanding the molecular regulation of this polygenic trait is crucial for the eventual success of rice molecular breeding programmes. microRNAs have received tremendous attention recently due to its importance in negative regulation. In plants, apart from regulating developmental and physiological processes, microRNAs have also been associated with different biotic and abiotic stresses. Hence here we chose to analyze the differential expression profiles of microRNAs in three drought treated rice varieties: Vandana (drought-tolerant), Aday Sel (drought-tolerant) and IR64 (drought-susceptible) in greenhouse conditions via high-throughput sequencing.
    Matched MeSH terms: Computational Biology/methods
  17. Fulltext Molecular epidemiology of coxsackievirus A16: intratype and prevalent intertype recombination identified
    Chen X, Tan X, Li J, Jin Y, Gong L, Hong M, et al.
    PLoS One, 2013;8(12):e82861.
    PMID: 24340064 DOI: 10.1371/journal.pone.0082861
    Coxsackievirus A16 (CVA16) is responsible for nearly 50% of all the confirmed hand, foot, and mouth disease (HFMD) cases in mainland China, sometimes it could also cause severe complications, and even death. To clarify the genetic characteristics and the epidemic patterns of CVA16 in mainland China, comprehensive bioinfomatics analyses were performed by using 35 CVA16 whole genome sequences from 1998 to 2011, 593 complete CVA16 VP1 sequences from 1981 to 2011, and prototype strains of human enterovirus species A (EV-A). Analysis on complete VP1 sequences revealed that subgenotypes B1a and B1b were prevalent strains and have been co-circulating in many Asian countries since 2000, especially in mainland China for at least 13 years. While the prevalence of subgenotype B1c (totally 20 strains) was much limited, only found in Malaysia from 2005 to 2007 and in France in 2010. Genotype B2 only caused epidemic in Japan and Malaysia from 1981 to 2000. Both subgenotypes B1a and B1b were potential recombinant viruses containing sequences from other EV-A donors in the 5'-untranslated region and P2, P3 non-structural protein encoding regions.
    Matched MeSH terms: Computational Biology/methods*
  18. Fulltext birgHPC: creating instant computing clusters for bioinformatics and molecular dynamics
    Chew TH, Joyce-Tan KH, Akma F, Shamsir MS
    Bioinformatics, 2011 May 1;27(9):1320-1.
    PMID: 21398666 DOI: 10.1093/bioinformatics/btr109
    birgHPC, a bootable Linux Live CD has been developed to create high-performance clusters for bioinformatics and molecular dynamics studies using any Local Area Network (LAN)-networked computers. birgHPC features automated hardware and slots detection as well as provides a simple job submission interface. The latest versions of GROMACS, NAMD, mpiBLAST and ClustalW-MPI can be run in parallel by simply booting the birgHPC CD or flash drive from the head node, which immediately positions the rest of the PCs on the network as computing nodes. Thus, a temporary, affordable, scalable and high-performance computing environment can be built by non-computing-based researchers using low-cost commodity hardware.
    Matched MeSH terms: Computational Biology/methods*
  19. Dissecting Candida albicans Infection from the Perspective of C. albicans Virulence and Omics Approaches on Host-Pathogen Interaction: A Review
    Chin VK, Lee TY, Rusliza B, Chong PP
    Int J Mol Sci, 2016 Oct 18;17(10).
    PMID: 27763544
    Candida bloodstream infections remain the most frequent life-threatening fungal disease, with Candida albicans accounting for 70% to 80% of the Candida isolates recovered from infected patients. In nature, Candida species are part of the normal commensal flora in mammalian hosts. However, they can transform into pathogens once the host immune system is weakened or breached. More recently, mortality attributed to Candida infections has continued to increase due to both inherent and acquired drug resistance in Candida, the inefficacy of the available antifungal drugs, tedious diagnostic procedures, and a rising number of immunocompromised patients. Adoption of animal models, viz. minihosts, mice, and zebrafish, has brought us closer to unraveling the pathogenesis and complexity of Candida infection in human hosts, leading towards the discovery of biomarkers and identification of potential therapeutic agents. In addition, the advancement of omics technologies offers a holistic view of the Candida-host interaction in a non-targeted and non-biased manner. Hence, in this review, we seek to summarize past and present milestone findings on C. albicans virulence, adoption of animal models in the study of C. albicans infection, and the application of omics technologies in the study of Candida-host interaction. A profound understanding of the interaction between host defense and pathogenesis is imperative for better design of novel immunotherapeutic strategies in future.
    Matched MeSH terms: Computational Biology/methods*
  20. Fulltext Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review
    Chong LC, Gandhi G, Lee JM, Yeo WWY, Choi SB
    Int J Mol Sci, 2021 Aug 20;22(16).
    PMID: 34445667 DOI: 10.3390/ijms22168962
    Spinal muscular atrophy (SMA), one of the leading inherited causes of child mortality, is a rare neuromuscular disease arising from loss-of-function mutations of the survival motor neuron 1 (SMN1) gene, which encodes the SMN protein. When lacking the SMN protein in neurons, patients suffer from muscle weakness and atrophy, and in the severe cases, respiratory failure and death. Several therapeutic approaches show promise with human testing and three medications have been approved by the U.S. Food and Drug Administration (FDA) to date. Despite the shown promise of these approved therapies, there are some crucial limitations, one of the most important being the cost. The FDA-approved drugs are high-priced and are shortlisted among the most expensive treatments in the world. The price is still far beyond affordable and may serve as a burden for patients. The blooming of the biomedical data and advancement of computational approaches have opened new possibilities for SMA therapeutic development. This article highlights the present status of computationally aided approaches, including in silico drug repurposing, network driven drug discovery as well as artificial intelligence (AI)-assisted drug discovery, and discusses the future prospects.
    Matched MeSH terms: Computational Biology/methods
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